Fengshuang Liu

Papers

Total Citations

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About

Dr. Fengshuang Liu is a rising researcher at the intersection of artificial intelligence and biomedical engineering, with a primary focus on brain-computer interfaces and precision agriculture. Her most cited work, "3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification" (2022, 14 citations), introduces a novel deep learning architecture that significantly improves the decoding of motor imagery from electroencephalography (EEG) signals—a critical step for non-invasive neural prosthetics. By integrating multiscale spatial and temporal features, this approach enhances classification accuracy, offering a robust framework for real-time BCI applications. More recently, Dr. Liu has extended her expertise to agricultural technology, co-authoring "An Attention-Based Spatial-Spectral Joint Network for Maize Hyperspectral Images Disease Detection" (2024). This work addresses the urgent need for early pest detection in maize crops, leveraging hyperspectral imaging and attention mechanisms to capture subtle spectral-spatial signatures of disease. While still early in its citation trajectory, this paper demonstrates her versatility in applying advanced neural networks to solve pressing real-world problems. Dr. Liu’s contributions are paving the way for more intelligent, adaptive systems in both healthcare and sustainable agriculture.